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Unformatted text preview: Combinational Collaborative Filtering for Personalized Community Recommendation Wen-Yen Chen Computer Science University of California Santa Barbara, CA 93106 email@example.com Dong Zhang Google Research, Beijng No. 1 Zhongguancun E. Road Beijing 100084, China firstname.lastname@example.org Edward Y. Chang Google Research Mountain View, CA 94043 email@example.com ABSTRACT Rapid growth in the amount of data available on social net- working sites has made information retrieval increasingly challenging for users. In this paper, we propose a collabora- tive filtering method, Combinational Collaborative Filtering (CCF), to perform personalized community recommenda- tions by considering multiple types of co-occurrences in so- cial data at the same time. This filtering method fuses se- mantic and user information, then applies a hybrid training strategy that combines Gibbs sampling and Expectation- Maximization algorithm. To handle the large-scale dataset, parallel computing is used to speed up the model training. Through an empirical study on the Orkut dataset, we show CCF to be both effective and scalable. Categories and Subject Descriptors H.4.m [ Information Systems Applications ]: Miscella- neous General Terms Algorithms, Experimentation Keywords Collaborative filtering, probabilistic models, personalized rec- ommendation 1. INTRODUCTION Social networking products are flourishing. Sites such as MySpace, Facebook, and Orkut attract millions of visitors a day, approaching the traffic of Web search sites . These social networking sites provide tools for individuals to es- tablish communities, to upload and share user generated content, and to interact with other users. In recent articles, users complained that they would soon require a full-time employee to manage their sizable social networks. Indeed, Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. To copy otherwise, to republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. KDD08, August 2427, 2008, Las Vegas, Nevada, USA. Copyright 2008 ACM 978-1-60558-193-4/08/08 ...$5.00. take Orkut as an example. Orkut enjoys 100+ million com- munities and users, with hundreds of communities created each day. A user cannot possibly view all communities to select relevant ones. In this work, we tackle the problem of community rec- ommendation for social networking sites. Such a problem fits in the framework of collaborative filtering (CF), which offers personal recommendations (of e.g., Web sites, books, or music) based on a users profile and prior information- access patterns. What differentiates our work from prior work is that we propose a fusion method, which combines information from multiple sources. We name our method CCF for...
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